Diffusion MRI tractography produces massive sets of streamlines that need to be clustered into anatomically meaningful white-matter bundles. Conventional clustering techniques group streamlines based on their proximity in Euclidean space. We have developed AnatomiCuts, an unsupervised method for clustering tractography streamlines based on their neighboring anatomical structures, rather than their coordinates in Euclidean space. In this work, we show that the anatomical similarity metric used in AnatomiCuts can be extended to find corresponding clusters across subjects and across hemispheres, without inter-subject or inter-hemispheric registration. Our proposed approach enables group-wise tract cluster analysis, as well as studies of hemispheric asymmetry. We evaluate our approach on data from the pilot MGH-Harvard-USC Lifespan Human Connectome project, showing improved correspondence in tract clusters across 184 subjects aged 8-90. Our method shows up to 38% improvement in the overlap of corresponding clusters when comparing subjects with large age differences. The techniques presented here do not require registration to a template and can thus be applied to populations with large inter-subject variability, e.g., due to brain development, aging, or neurological disorders.

中文翻译：

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跨受试者整个生命周期的扩散 MRI 纤维束成像数据的免注册分析

扩散 MRI 纤维束成像会产生大量的流线，这些流线需要聚集成具有解剖学意义的白质束。传统的聚类技术基于流线在欧几里得空间中的接近程度对流线进行分组。我们开发了 AnatomiCuts，这是一种基于相邻解剖结构而不是欧几里得空间中的坐标对纤维束成像流线进行聚类的无监督方法。在这项工作中，我们表明 AnatomiCuts 中使用的解剖相似性度量可以扩展到跨受试者和跨半球找到相应的集群，而无需跨受试者或跨半球注册。我们提出的方法可以进行分组道聚类分析，以及半球不对称性的研究。我们根据 MGH-Harvard-USC Lifespan Human Connectome 试点项目的数据评估了我们的方法，结果显示 184 名 8-90 岁受试者的道群中的对应性得到改善。在比较年龄差异较大的受试者时，我们的方法显示相应集群的重叠度提高了 38%。此处介绍的技术不需要注册到模板，因此可以应用于具有较大主体间变异性的人群，例如，由于大脑发育、衰老或神经系统疾病。